Processing continuous knearest neighbor knn query over moving objects in locationdependent application requires that the frequent location updates of moving objects and intensive continuous knn queries must be efficiently processed at the same time. In this method, the objects azimuth information is adopted to determine the. Here, poz r is the pdf that the distance from oz to query point q is r. Continuous knearest neighbor query over moving objects in road. Existing studies on the vk nn query have focused on static data objects. A practical method is to partition the data space into grid cells, with both object and query table being indexed by this grid structure, while solving the problem by periodically joining cells of objects with queries having. Realtime processing of k nn queries over moving objects. For example, the results of a query are to be recomputed whenever the query changes its location. Continuous reverse k nearest neighbor monitoring on moving objects in road networks. Moving range k nearest neighbor queries with quality. Probabilistic threshold k nearest neighbor queries over. Continuous reverse k nearest neighbor monitoring on moving.
In proceedings of ieee international conference on data engineering icde tokyo, japan, apr. Theknnsofeachqueryisrecomputedrepeatedlywith time interval weuseabufferobj curr to model the current positions of the objects. Tj in addition, each point in nnj is a nearest neighbor to q at each time point during time interval tj, and rnnj is the set of the reverse nearest neighbors to q at each time point during time interval tj. Monitoring of k nearest neighbors kyriakos mouratidis, dimitris papadias, spiridon bakiras,member, ieee, and yufei tao abstractassume a set of moving objects and a central server that monitors their positions over time, while processing continuous nearest neighbor queries from geographically distributed clients. However, the performance tends to degrade with time due to the dete. However, this work still needs a central server for monitoring the nn objects, maintaining the hugespatialdata,andbroadcastingeachobject sinformation. An important class of queries that is definitely useful for mod processing is the socalled k nearest neighbor knn queries, where one is interested in finding the k closest trajectories to a predefined query object q. Given a time interval ts, te and a moving query object q, a cknn query is to find the k nearest. The challenge of monitoring the kpnn for an arbitrarily moving user is to dynamically determine the update locations and then refresh the kpnn e. Many existing approach in these works, treebased indexes and grid index are mainly utilized to maintain a large volume of moving objects and improve the performance of search algorithms. Nearest neighbor nn queries in trajectory databases have received significant attention in the past, due to their applications in spatiotemporal data analysis. At each reevaluation time, by processing the object updates, the query updates and edge updates, the query results may be.
The high potential of reducing the query processing cost as well as the large spectrum of associated applications have attracted considerable attention to. In this paper, we propose a grid cell based continuous knn query processing method cknn. The continuous monitoring of range queries 4, 12, 2, k nearest neighbor knn queries, 22, 20, 9, 18 and reverse nearest neighbor queries 10, 19, 3 has been widely studied in recent past. Continuous nearest neighbor queries over sliding windows. Request pdf efficiently monitoring reverse knearest neighbors in spatial networks given a set of clients c, a set of facilities f and a query q. We present a framework for continuous reverse k nearest neighbor rknn. Continuous kmeans monitoring over moving objects zhenjie zhang, yin yang, anthony k.
Monitoring knearest neighbor queries over moving objects. Directionaware continuous moving knearestneighbor query in. Central to many locationbased service applications is the task of processing knearest neighbor knn queries over moving objects. Qindex 19 monitors static range queries over moving objects. We present a framework for continuous reverse k nearest. In this paper, we study the problem of continuous monitoring of reverse k nearest neighbors queries in euclidean space as well as in spatial networks. A report on law and policy in the united states, the europrean union, and japan. Continuous k nearest neighbor queries on moving objects as a major type of continuous spatial queries, the continuous k nearest neighbor k nn query on moving objects has been studied extensively. Mobieyes 6 and mqm 3 utilize the computational capabilities of the data objects to reduce the load at the central server.
Many locationbased applications require constant monitoring of k nearest neighbor knn queries over moving objects within a geographic area. The problem of continuously monitoring multiple k nearest neighbor k nn queries with dynamic object and query dataset is valuable for many locationbased applications. Therefore, in this study, we devise a probabilistic algorithm, called mint, for moving range knn queries with quality guarantee over uncertain moving objects, which determines approximate answers using a probability density function pdf and a cumulative distribution function cdf of the distance between a query issuer and an uncertain moving object. In the context of certain trajectory databases there is not a common definition of nearest neighbor queries, but rather a set of different interpretations. The first one is based on indexing the ob jects themselves and we refer to it as object. Efficient algorithm to monitor continuous knn queries. Maintaining sliding window skylines on data streams. Efficient processing of continuous reverse k nearest. We propose two methods to monitor knn queries over moving objects. The high potential of reducing the query processing cost as well as the large spectrum of associated applications have attracted considerable attention to this query type from the database community. Therefore, in this study, we devise a probabilistic algorithm, called mint, for moving range knn queries with quality guarantee over uncertain moving objects, which determines approximate answers using a probability density function pdf and a cumulative distribution function cdf of the distance between a query issuer and an uncertain. Many locationbased applications require constant monitoring of k nearest neighbor k nn queries over moving objects within a geographic area. Koudas, monitoring k nearest neighbor queries over moving objects, in. In 1, given a query trajectory or spatial point q and a time interval t, a.
In locationbased advertising, in order to make the best use of available band. As the user is moving and may not always follow the shortest path, the query path keeps changing. Continuous visible k nearest neighbor query on moving objects. Scalable processing of continuous knearest neighbor queries. Processing continuous k nearest neighbor queries in. Nearest and reverse nearest neighbor queries for moving. Probabilistic nearest neighbor queries on uncertain moving object trajectories johannes niedermayer, andreas zu. Continuous knearest neighbor cknn queries on moving objects retrieve the. In this paper, we introduce and solve constrained knearest neighbor cknn queries and historical continuous cknn hccknn queries on rtreelike structures storing historical information about moving. An important query for spatiotemporal databases is to find nearest trajectories of moving objects.
Existing approaches to this problem have focused on predictive queries, and relied on the assumption that the trajectories of the objects are fully predictable at query processing time. This technique is commonly used in predictive analytics to estimate or classify a point based on the consensus of its neighbors. It indexes the ranges using an rtree and probes moving objects against the index in order to update the a. However, as far as we know, the existing knearest neighbor knn queries take distance as the major criteria for nearest neighbor objects, even without taking direction into consideration. However, as far as we know, the existing k nearest neighbor knn queries take distance as the major criteria for nearest neighbor objects, even without taking direction into consideration. Continuous k nearest neighbor queries on moving obas a major type of continuous spatial queries, the continuous k nearest neighbor knn query on moving objects has been studied extensively. Home browse by title proceedings icde 05 monitoring k nearest neighbor queries over moving objects. Monitoring knearest neighbor queries over moving objects core. Existing approaches to this problem have focused on predictive queries, and relied on the assumption that the trajectories of the. Reverse nearest neighbor queries are intimately related to nearest neighbor queries. Nearest and reverse nearest neighbor queries for moving objects 3 tt. Continuous knearest neighbor processing based on speed and. In this paper, we introduce and solve constrained k nearest neighbor cknn queries and historical continuous cknn hccknn queries on rtreelike structures storing historical information about moving.
The problem of continuously monitoring multiple knearest neighbor knn queries with dynamic object and query dataset is valuable for many locationbased applications. A generic framework for monitoring continuous spatial queries. Existing stateoftheart algorithms have focused primarily on handling mknn queries in undirected and static spatial networks where every edge is undirected and its weight does not change over time. May 15, 2011 in this paper, we study the problem of continuous monitoring of reverse k nearest neighbors queries in euclidean space as well as in spatial networks. Continuous knearest neighbor cknn queries on moving. Experimental results show that casper achieves high quality locationbased services while providing anonymity for both data and queries. A safe region based approach to moving knn queries in. Continuous k nearest neighbor cknn query is an important type of spatiotemporal queries. In the gaming example, a player naturally wishes to keep track of the k nearby players in order to make a combat plan. Efficiently monitoring reverse knearest neighbors in. The existing research on continuous monitoring of spatial queries has focused exclusively on euclidean spaces. The moving k nearest neighbor mknn query finds the k nearest neighbors of a moving query point continuously.
Then, we discuss the proposals related to reverse nearest neighbor queries. Algorithms for constrained k nearest neighbor queries over. Vague continuous knearest neighbor queries over moving. Nearest neighbor and reverse nearest neighbor queries for. Scalable distributed processing of k nearest neighbour queries. The first one adapts conceptual partitioning mhp05, the best existing method for nn monitoring over update streams, to the sliding window model. Probabilistic nearest neighbor queries on uncertain moving. Reversenearest neighbor queries on uncertain moving. This paper proposes a generic framework for monitoring continuous spatial queries over moving objects. Monitoring knearest neighbor queries over moving objects ieee. For these uncertain databases, an important query is the probabilistic k nearest neighbor query k pnn, which computes the probabilities of sets ofk objects for being the closest to a given query point. Visible reverse knearest neighbor queries request pdf. Algorithms for nearest neighbor search on moving object.
Existing techniques are sensitive toward objects and queries movement. A set of query points in the region is given, and for each query, we periodically monitor its k nearest neighbors among the objects in the region of interest. In 1, given a query trajectory or spatial point q and a time interval t, a nn query returns either the trajectory from. The framework distinguishes itself from existing work by being the.
In that sense, the query answer to the aggregate knearestneighbor query needs to be defined through an aggregate to define how an object is considered closeby to the set of multiple points. The new casper proceedings of the 32nd international. Nearest neighbor and reverse nearest neighbor queries for moving objects article in the vldb journal 153. Monitoring nearest neighbor queries with cache strategies. The framework distinguishes itself from existing work by being the first to address the location update issue and to provide a common interface for monitoring mixed types of queries. In this paper, we present an efficient method for continuous monitoring of reverse k nearest neighbor queries crknn in road networks where both query objects and moving objects can roam arbitrarily in a road network. Continuous knearest neighbor search for moving objects. For example, support for k nearest neighbor knn queries over indoor moving objects enables the detection of approaching potential threats at sensitive locations in a subway.
Koudas, monitoring knearest neighbor queries over moving objects, in. The process of moving k nearest neighbor mknn queries has been studied extensively in spatial network databases. Recently more and more people focus on k nearest neighbor knn query processing over moving objects in road networks, e. In this paper, we study the problem of moving knn queries over static data objects, i. In this paper, we present an efficient method for continuous monitoring of reverse k nearest neighbor queries crknn in road networks where both query objects and moving objects can roam arbitrarily in. Dbms that supports moving objects has to present robust behavior in the above mentioned issues. Their frameworks process the queries incrementally by monitoring the effect of each object location update on the query answer. Recently more and more people focus on knearest neighbor knn query processing over moving objects in road networks, e. The knn problem on moving objects is receiving increasing attention in the research community. In order to find the top knearest objects moving toward a query point, this paper presents a novel algorithm for directionaware knn daknn queries for moving objects in a road network.
Tung, and dimitris papadias abstract given a dataset p, a kmeans query returns k points in space called centers, such that the average squared distance between each point in p and its nearest center is minimized. A c knn query is to find among all moving objects the knearest neighbors. Monitoring k nearest neighbor queries over moving objects. A safe exit algorithm for moving k nearest neighbor queries. Based on the notion of safe region, the client location update. The work in this category concerns with the applications of the linearitybased prediction models to answer nearest neighbor queries 26, k nearest and reverse k nearest neighbor queries 3, or. In recent years, considerable research has been conducted into monitoring reverse k nearest neighbor queries. Aggregate nearest neighbor queries in spatial databases acm. A visible k nearest neighbor vk nn query retrieves k objects that are visible and nearest to the query object, where visible means that there is no obstacle between an object and the query object. Evaluating probability threshold knearestneighbor queries. Visible k nearest neighbor queries over uncertain data. Qindex avoids the expensive due to intensive updates maintenance of an index on the objects. Continuous reverse k nearest neighbors queries in euclidean.
Probabilistic threshold k nearest neighbor queries over moving. Apr 28, 2009 an important query for spatiotemporal databases is to find nearest trajectories of moving objects. Many locationbased applications require constant monitoring of knearest neighbor knn queries over moving objects within a geographic area. Existing work on this topic focuses on the closest trajectories in the whole data space. In this paper, we study the problem of continuous reverse nearest neighbor queries where both the query object q and data objects are moving. In that sense, the query answer to the aggregate k nearest neighbor query needs to be defined through an aggregate to define how an object is considered closeby to the set of multiple points. This paper presents and compares two techniques for nn monitoring over sliding windows, covering both countbased and timebased windows, arbitrary k, and static or moving queries. Continuous nearest neighbor monitoring in road networks. Mobile objects freely move in and out of the region.
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